---
title: Pinecone Rerank
---

> This notebook shows how to use **PineconeRerank** for two-stage vector retrieval reranking using Pinecone's hosted reranking API as demonstrated in `langchain_pinecone/libs/pinecone/rerank.py`.

## Setup

Install the `langchain-pinecone` package.

```python
%pip install -qU "langchain-pinecone"
```

## Credentials

Set your Pinecone API key to use the reranking API.

```python
import os
from getpass import getpass

os.environ["PINECONE_API_KEY"] = os.getenv("PINECONE_API_KEY") or getpass(
    "Enter your Pinecone API key: "
)
```

## Instantiation

Use `PineconeRerank` to rerank a list of documents by relevance to a query.

```python
from langchain_core.documents import Document
from langchain_pinecone import PineconeRerank

# Initialize reranker
reranker = PineconeRerank(model="bge-reranker-v2-m3")

# Sample documents
documents = [
    Document(page_content="Paris is the capital of France."),
    Document(page_content="Berlin is the capital of Germany."),
    Document(page_content="The Eiffel Tower is in Paris."),
]

# Rerank documents
query = "What is the capital of France?"
reranked_docs = reranker.compress_documents(documents, query)

# Print results
for doc in reranked_docs:
    score = doc.metadata.get("relevance_score")
    print(f"Score: {score:.4f} | Content: {doc.page_content}")
```

```output
/Users/jakit/customers/aurelio/langchain-pinecone/libs/pinecone/.venv/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
  from .autonotebook import tqdm as notebook_tqdm
```
```output
Score: 0.9998 | Content: Paris is the capital of France.
Score: 0.1950 | Content: The Eiffel Tower is in Paris.
Score: 0.0042 | Content: Berlin is the capital of Germany.
```

## Usage

### Reranking with Top-N

Specify `top_n` to limit the number of returned documents.

```python
# Return only top-1 result
reranker_top1 = PineconeRerank(model="bge-reranker-v2-m3", top_n=1)
top1_docs = reranker_top1.compress_documents(documents, query)
print("Top-1 Result:")
for doc in top1_docs:
    print(f"Score: {doc.metadata['relevance_score']:.4f} | Content: {doc.page_content}")
```

```output
Top-1 Result:
Score: 0.9998 | Content: Paris is the capital of France.
```

## Reranking with Custom Rank Fields

If your documents are dictionaries or have custom fields, use `rank_fields` to specify the field to rank on.

```python
# Sample dictionary documents with 'text' field
docs_dict = [
    {
        "id": "doc1",
        "text": "Article about renewable energy.",
        "title": "Renewable Energy",
    },
    {"id": "doc2", "text": "Report on economic growth.", "title": "Economic Growth"},
    {
        "id": "doc3",
        "text": "News on climate policy changes.",
        "title": "Climate Policy",
    },
]

# Initialize reranker with rank_fields
reranker_text = PineconeRerank(model="bge-reranker-v2-m3", rank_fields=["text"])
climate_docs = reranker_text.rerank(docs_dict, "Latest news on climate change.")

# Show IDs and scores
for res in climate_docs:
    print(f"ID: {res['id']} | Score: {res['score']:.4f}")
```

```output
ID: doc3 | Score: 0.9892
ID: doc1 | Score: 0.0006
ID: doc2 | Score: 0.0000
```

We can rerank based on title field

```python
economic_docs = reranker_text.rerank(docs_dict, "Economic forecast.")

# Show IDs and scores
for res in economic_docs:
    print(
        f"ID: {res['id']} | Score: {res['score']:.4f} | Title: {res['document']['title']}"
    )
```

```output
ID: doc2 | Score: 0.8918 | Title: Economic Growth
ID: doc3 | Score: 0.0002 | Title: Climate Policy
ID: doc1 | Score: 0.0000 | Title: Renewable Energy
```

## Reranking with Additional Parameters

You can pass model-specific parameters (e.g., `truncate`) directly to `.rerank()`.

How to handle inputs longer than those supported by the model. Accepted values: END or NONE.
END truncates the input sequence at the input token limit. NONE returns an error when the input exceeds the input token limit.

```python
# Rerank with custom truncate parameter
docs_simple = [
    {"id": "docA", "text": "Quantum entanglement is a physical phenomenon..."},
    {"id": "docB", "text": "Classical mechanics describes motion..."},
]

reranked = reranker.rerank(
        documents=docs_simple,
        query="Explain the concept of quantum entanglement.",
        truncate="END",
)
# Print reranked IDs and scores
for res in reranked:
    print(f"ID: {res['id']} | Score: {res['score']:.4f}")
```

```output
ID: docA | Score: 0.6950
ID: docB | Score: 0.0001
```

## Use within a chain

## API reference

- `PineconeRerank(model, top_n, rank_fields, return_documents)`
- `.rerank(documents, query, rank_fields=None, model=None, top_n=None, truncate="END")`
- `.compress_documents(documents, query)` (returns `Document` objects with `relevance_score` in metadata)
